Intelligent traffic cooperative control optimization method, device and equipment based on quantum key distribution and digital twinning

Abstract

The embodiment of the invention discloses an intelligent traffic cooperative control optimization method, device and equipment based on quantum key distribution and digital twinning, and relates to the technical field of intelligent traffic. The method comprises the following steps: transmitting traffic data and spatial information through the quantum secure communication network, and constructing a traffic digital twin mapping model based on the traffic data and the spatial information; the traffic digital twin mapping model is configured to map to obtain a physical traffic system operation state corresponding to the traffic data and the spatial information; accessing the digital twin mapping model into a quantum computing environment, and generating a traffic control strategy based on the operation state of a physical traffic system by using a preset quantum algorithm; and transmitting the traffic control strategy to traffic equipment in a physical traffic system for execution, synchronously transmitting the traffic control strategy to a digital twin mapping model for simulation execution, and optimizing and adjusting the traffic control strategy according to a difference comparison result of a feedback actual execution result and a simulation execution result.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation, and particularly to an intelligent transportation collaborative control optimization method, device and equipment based on quantum key distribution and digital twin. Background Art

[0002] With the continuous expansion of the scale of road traffic and the increasing complexity, the intelligent transportation system puts forward higher requirements for the security of data transmission and the accuracy of control strategies. The traffic system needs to collect and transmit a large amount of sensitive information including vehicle positions, speeds, flows, etc. in real time, and at the same time generate traffic control strategies based on these data to realize the collaborative management and control of traffic devices such as traffic lights and variable signs, so as to ensure the smooth and orderly flow of traffic.

[0003] However, there are some defects in the existing intelligent transportation system in terms of data transmission and control strategy generation. For example, traffic data transmission still relies on classical encryption algorithms for transmission, which is more difficult to resist increasingly complex network attacks. Especially, there is a risk that control strategy data is stolen and tampered with during the transmission process, resulting in the inability to guarantee the integrity and authenticity of the data. Due to the possible problems with the reliability of the received data, the traffic control strategies generated based on the transmitted data may be difficult to accurately adapt to the actual traffic conditions, leading to low efficiency in the operation and management of the traffic system. Summary of the Invention

[0004] In view of this, the present invention aims to provide an intelligent transportation collaborative control optimization method, device and equipment based on quantum key distribution and digital twin, which is convenient for realizing the dynamic optimization of traffic control strategies, thereby improving the operation and management efficiency of the traffic system.

[0005] To achieve the above object, the following technical solutions are adopted: In the first aspect, an embodiment of the present invention provides a traffic collaborative control optimization method based on quantum key distribution and digital twin, the method comprising: Deploy quantum communication devices at relevant nodes of the physical traffic system, and use quantum key distribution technology to establish a communication link to form a quantum secure communication network; Transmit traffic data and spatial information through the quantum secure communication network, and construct a traffic digital twin mapping model based on the traffic data and spatial information; the traffic digital twin mapping model is configured to map to obtain the operating state of the physical traffic system corresponding to the traffic data and spatial information.

[0006] Connect the digital twin mapping model to a quantum computing environment, and use a preset quantum algorithm to generate a traffic control strategy based on the operating state of the physical traffic system.

[0007] Transmit the traffic control strategy to traffic devices in the physical traffic system through a quantum-secure communication network, and simultaneously transmit it to the digital twin mapping model to simulate the execution of the traffic control strategy on the corresponding traffic devices in the physical system. Optimize and adjust the traffic control strategy according to the comparison result of the difference between the actual execution result and the simulated execution result fed back.

[0008] Optionally, deploy quantum communication devices at relevant nodes in the physical traffic system, and use quantum key distribution technology to establish a communication link to form a quantum-secure communication network, including: Select key nodes in the traffic system and install quantum communication devices with single-photon emission and reception functions at the key nodes; among them, the key nodes at least include: intersection control centers, designated road section monitoring points, and transportation hub stations, and the quantum communication devices support polarization state modulation and basis vector matching measurement functions; Use a true random number generator to generate multiple random control parameters, and dynamically regulate the polarization state of photons emitted by the single-photon source based on the random control parameters, so that the polarization state of photons presents at least one of the phases of horizontal, vertical, +45°, and -45°; According to the predetermined decoy state protocol, the transmitting end of the quantum communication device randomly sends signal-state photons and decoy-state photons according to a preset ratio; among them, the signal-state photons include the above four polarization state phases and are used to generate quantum keys, and the decoy-state photons are used for data security monitoring; Perform polarization state measurement on the received photons based on the polarization state phase synchronized with the transmitting end, and screen out valid data according to the basis vector matching rule; among them, the basis vector matching rule is configured to screen and retain photons with the polarization state phase of the transmitting end matching the polarization state phase of the receiving end; Analyze the bit error rate and gain of the signal-state and decoy-state photons in the valid data. If the bit error rate of the decoy-state photons increases compared with the bit error rate of the signal-state photons, and the gain of the decoy-state photons decreases, it is determined that there is a wiretapping risk in the channel, and the generation and distribution of the current quantum key are interrupted; If the channel is determined to be secure, generate a shared quantum key based on the signal-state photons in the valid data; Establish an end-to-end quantum-secure communication link according to the shared quantum key.

[0009] Optionally, the traffic data includes: vehicle position, speed, and traffic flow, and the spatial information includes: road geometric structure; Transmit traffic data and spatial information through the quantum-secure communication network, and construct a traffic digital twin mapping model based on the traffic data and spatial information, including: Perform integrity verification on the received traffic data and spatial information based on the quantum encryption hash algorithm to check whether the data has been tampered with during transmission; Integrate data based on verified traffic data and spatial information, and construct a digital twin mapping model for real-time mapping the operating state of the physical traffic system, where the operating state of the traffic system includes: traffic flow distribution, signal light working state, and road traffic conditions.

[0010] Optionally, integrate data based on verified traffic data and spatial information, and construct a digital twin mapping model for real-time mapping the operating state of the physical traffic system, including: performing multi-source data calibration processing on the verified traffic data and spatial information based on timestamps to make the traffic data and spatial information in the same spatio-temporal reference system; Perform sliding window statistics on the calibrated dynamic traffic data by section, calculate vehicle position, average vehicle speed, and traffic flow volatility characteristics within a predetermined time interval; and extract lane number, road curvature, and slope characteristics from the road geometric structure, perform dimensionality reduction processing on the obtained characteristics, and fuse them to generate a feature vector group containing each of the characteristics; Based on the feature vector group, construct a node-edge network model of the traffic system in the form of a graph structure of nodes and edges, where the nodes include intersections and ramps, and the edges include sections and the lane number and speed limit attributes associated with the sections; Perform spatial coordinate mapping on the node-edge network based on a preset 3D modeling technique to generate a 3D road network framework containing geographic information; On the basis of the 3D road network framework, introduce a cellular automaton model to simulate vehicle following and lane-changing behaviors, establish a mapping relationship between signal phases and intersection nodes, and combine the influence information of the road geometric structure on traffic flow to generate an initial signal control strategy, initially forming a digital twin mapping model for real-time mapping the operating state of the physical traffic system; where the digital twin mapping model contains vehicle movement rules and traffic equipment control logic.

[0011] Optionally, synchronously transmit to the digital twin mapping model to simulate the execution of the traffic control strategy on the corresponding traffic equipment in the physical system, and optimize and adjust the traffic control strategy according to the difference comparison result between the actual execution result and the simulated execution result, including: Load traffic control strategy parameters, simulate the process of traffic flow distribution and signal light state change after the strategy execution, and generate simulated execution result data including virtual vehicle trajectories and intersection passing efficiency; Align the timestamps of the actual execution result data and the simulated execution result data, and match the virtual and real data sequences based on the dynamic time warping algorithm; Calculate the difference comparison index between the actual execution result and the simulated execution result according to three dimensions: the traffic flow distribution matching degree, the signal control efficiency, and the path planning effect. Among them, the traffic flow distribution matching degree is used to represent the spatial overlap rate between the actual congestion area and the simulated congestion area, the signal control efficiency is used to represent the difference in the average vehicle delay time between the actual execution and the simulated execution, and the path planning effect is used to represent the deviation distance between the actual navigation path and the simulated optimal path. Optimize and adjust the traffic control strategy parameters according to the difference comparison index. The traffic control strategy parameters include signal timing plans and path planning instructions.

[0012] Optionally, the control strategy parameters need to be consistent between the physical end and the virtual end.

[0013] In a second aspect, an embodiment of the present invention also provides an intelligent transportation collaborative control device based on quantum key distribution and digital twins, including: A quantum communication network construction module for deploying quantum communication devices at relevant nodes of the physical transportation system, using quantum key distribution technology to establish a communication link, and forming a quantum secure communication network; A model construction module for transmitting traffic data and spatial information through the quantum secure communication network, and constructing a traffic digital twin mapping model based on the traffic data and spatial information. The traffic digital twin mapping model is configured to map and obtain the operating state of the physical transportation system corresponding to the traffic data and spatial information.

[0014] A strategy generation module for connecting the digital twin mapping model to a quantum computing environment and generating a traffic control strategy based on the operating state of the physical transportation system using a preset quantum algorithm.

[0015] A strategy execution optimization module for transmitting the traffic control strategy to traffic devices in the physical transportation system through the quantum secure communication network for execution, and synchronously transmitting it to the digital twin mapping model to simulate the execution of the traffic control strategy on the corresponding traffic devices in the physical system. Optimize and adjust the traffic control strategy according to the difference comparison result between the actual execution result and the simulated execution result fed back.

[0016] Optionally, the quantum communication network construction module includes: A device deployment unit for selecting key nodes in the traffic system and installing quantum communication devices with single-photon emission and reception functions at the key nodes. Among them, the key nodes at least include: intersection control centers, designated section monitoring points, and transportation hub stations, and the quantum communication devices support polarization state modulation and basis vector matching measurement functions; The polarization state control unit is used to generate a plurality of random control parameters by using a true random number generator, and dynamically control the polarization state of photons emitted by a single photon source based on the random control parameters, so that the polarization state of photons presents at least one of the phases of horizontal, vertical, +45°, and -45°; The photon sending unit is used to randomly send signal state photons and decoy state photons according to a predetermined decoy state protocol and a preset ratio at the transmitting end of the quantum communication device; wherein, the signal state photons include the above four polarization state phases and are used to generate quantum keys, and the decoy state photons are used for data security monitoring; The measurement and screening unit is used to perform polarization state measurement on received photons based on the polarization state phase synchronized with the sending end, and screen out valid data according to the basis vector matching rule; wherein, the basis vector matching rule is configured to screen and retain photons with matching polarization state phases between the sending end and the receiving end; The security determination unit is used to analyze the bit error rate and gain of the signal state and decoy state photons in the valid data. If the bit error rate of the decoy state photons increases compared with the bit error rate of the signal state photons, and the gain of the decoy state photons decreases, it is determined that there is a risk of eavesdropping on the channel, and the generation and distribution of the current quantum key are interrupted; if the channel is determined to be secure, a shared quantum key is generated based on the signal state photons in the valid data; The link establishment unit is used to establish an end-to-end quantum secure communication link according to the shared quantum key.

[0017] Optionally, the traffic data includes: vehicle position, speed, and traffic flow, and the spatial information includes: road geometric structure; the model construction module is specifically configured to: Perform integrity verification on the received traffic data and spatial information based on a quantum encryption hash algorithm to check whether the data is tampered with during transmission; Integrate the verified traffic data and spatial information to construct a digital twin mapping model for real-time mapping of the operating state of the physical traffic system, and the operating state of the traffic system includes: traffic flow distribution, signal light working state, and road traffic conditions.

[0018] In a third aspect, an embodiment of the present invention further provides an electronic device, which includes: a housing, a processor, a memory, a circuit board, and a power supply circuit. Among them, the circuit board is arranged inside the space surrounded by the housing, and the processor and the memory are arranged on the circuit board; the power supply circuit is used to supply power to each circuit or device of the above electronic device; the memory is used to store executable program codes; the processor runs a program corresponding to the executable program codes by reading the executable program codes stored in the memory, and is used to execute the method according to any one of the foregoing first aspects.

[0019] Compared with the prior art, the intelligent transportation collaborative control optimization method, device, and equipment based on quantum key distribution and digital twin provided by the embodiments of the present invention deploy quantum communication devices at relevant nodes of the physical transportation system, use quantum key distribution technology to establish a quantum secure communication network, and provide reliable security guarantees for the transmission of traffic data and spatial information, fundamentally solving the security risks of traditional encryption methods in the face of complex network attacks, and ensuring the authenticity and reliability of the data of the digital twin mapping model used to construct the operating state of the mapped physical transportation system. Then, the digital twin mapping model is connected to the quantum computing environment, and a traffic control strategy is generated using a preset quantum algorithm. The traffic control strategy is synchronously transmitted to the traffic devices of the physical transportation system and the digital twin mapping model through the quantum secure communication network. While the strategy is executed on the physical device, it is also simulated and executed in the digital twin mapping model. By comparing the differences between the actual execution results and the simulated execution results, problems that may exist in the actual traffic control application of the control strategy can be discovered in a timely manner, and the strategy can be optimized and adjusted, realizing the dynamic optimization of the traffic control strategy and enabling the control strategy to be adjusted in a timely manner according to the real-time changes of the traffic system, thereby significantly improving the operating efficiency of the traffic system, improving the utilization rate of road resources to a certain extent, and alleviating traffic congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a schematic flowchart of an embodiment of the intelligent transportation collaborative control optimization method based on quantum key distribution and digital twin of the present invention; Figure 2 It is a schematic flowchart of another embodiment of the intelligent transportation collaborative control optimization method based on quantum key distribution and digital twin of the present invention; Figure 3 It is a schematic block diagram of an embodiment of the intelligent transportation collaborative control optimization device based on quantum key distribution and digital twin of the present invention; Figure 4 It is a schematic block diagram of an embodiment of the electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The embodiments of the present invention will be described in detail below with reference to the drawings.

[0023] It should be clear that in order to more clearly illustrate the present invention, numerous technical details are described in the following specific embodiments. Those skilled in the art should understand that the present invention can still be implemented without some of these details. Additionally, in order to highlight the innovative essence of the present invention, some methods, means, components, and their applications well-known to those skilled in the art are not described in detail, but this does not affect the implementation of the present invention. The embodiments described herein are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0024] The embodiment of the present application provides a traffic collaborative control optimization method based on quantum key distribution and digital twin, which is applied to scenarios such as traffic system control and highway driving navigation. By organically integrating quantum key distribution technology, digital twin technology, and quantum computing technology, a complete intelligent traffic collaborative control system is formed. Specifically, a quantum secure communication network is constructed to ensure data transmission, and by combining digital twin and quantum computing, real-time mapping and intelligent regulation of the traffic system are realized, solving the problems of insecure traffic data transmission and insufficient accuracy of control strategies in traditional intelligent traffic systems, thereby significantly improving the traffic operation management efficiency and safety.

[0025] As Figure 1 and Figure 2 shown, the traffic collaborative control optimization method based on quantum key distribution and digital twin provided in this embodiment includes the following steps: S1. Deploy quantum communication devices at relevant nodes of the physical traffic system, and use quantum key distribution technology to establish a communication link to form a quantum secure communication network; In the actual deployment process, first conduct a topology analysis of the target traffic system, combine traffic flow density, data transmission requirements, and key nodes to conduct importance assessment, and determine the deployment location. For example, in the urban traffic system, select the main road intersections with a traffic flow exceeding 5000 vehicles / hour, or the areas near large traffic hubs with a large daily pedestrian flow, as well as the traffic command centers that undertake the function of regional data aggregation as key nodes. In the scenario of the highway traffic system, devices can be deployed at positions such as toll stations, service areas, and tunnel entrances and exits.

[0026] The quantum communication device can select existing devices from the manufacturer according to needs. In this application, the quantum communication device adopts a modular design, integrating a single photon emission module, a receiving module, and a signal processing module. Among them, the single photon emission module is based on an InGaAs / InP single photon source, and the working temperature is stabilized at 20±0.5°C through a temperature control system to ensure that the single photon emission rate is stable at 10 9photons / second; the receiving module is equipped with a superconducting nanowire single-photon detector (SNSPD), and the detection efficiency can reach 60%, and the time jitter is less than 100 ps. The quantum communication device needs to support the duplex communication mode and can perform quantum key distribution and data transmission simultaneously.

[0027] S2: Transmit traffic data and spatial information through the quantum secure communication network, and construct a traffic digital twin mapping model based on the traffic data and spatial information; the traffic digital twin mapping model is configured to map and obtain the operating state of the physical traffic system corresponding to the traffic data and spatial information.

[0028] In this embodiment, traffic data collection uses a multi-source heterogeneous sensor network, including geomagnetic sensors deployed on roads, configured to collect vehicle flow and speed, and high-definition cameras, with built-in computer vision algorithms to identify vehicle positions and types. Spatial information is obtained through lidar to obtain the road geometric structure.

[0029] During the data transmission process, the quantum encryption hash algorithm is used to verify the integrity of the data. Among them, each data packet is attached with a 128-bit quantum hash value, and the receiving end quickly verifies whether the data has been tampered with through the quantum state superposition characteristic. After verifying the integrity and security, a traffic digital twin mapping model is constructed based on the obtained traffic data and spatial information.

[0030] S3: Connect the digital twin mapping model to the quantum computing environment, and use a preset quantum algorithm to generate a traffic control strategy based on the operating state of the physical traffic system.

[0031] The quantum computing environment can adopt a cloud quantum computing platform architecture, including a quantum bit processor, which integrates a preset quantum algorithm, specifically including a quantum annealing algorithm and a quantum genetic algorithm.

[0032] Among them, the quantum annealing algorithm is configured to solve the traffic signal timing optimization problem, encode parameters such as signal light phase switching and green signal ratio allocation into quantum bit states, and search for various timing schemes by simulating the quantum annealing process.

[0033] The quantum genetic algorithm is mainly applied to vehicle path planning. The road network is abstracted as a quantum chromosome, and genetic operators such as crossover and mutation are realized through quantum gate operations. Combining with the real-time traffic state, such as congestion index and accident location, a multi-objective optimization path is generated to assist in realizing the efficient scheduling of vehicles. Of course, the functions that can be realized in this embodiment will start corresponding programs according to different application scenarios or needs to realize functions that meet the scenarios or needs, and not all of them need to be enabled necessarily.

[0034] The digital twin mapping model can be connected to the quantum computing environment in the form of a data stream, and synchronously update the operation status data of the physical traffic system every predetermined time, including at least the traffic flow distribution map, the real-time phase of traffic lights, and the road section passing capacity. Based on the updated operation status data of the physical traffic system, real-time calculations are performed to output traffic control strategies including traffic signal timing plans, variable message sign display contents, and route guidance instructions.

[0035] S4. Transmit the traffic control strategy to the traffic equipment in the physical traffic system through the quantum secure communication network for execution, and synchronously transmit it to the digital twin mapping model to simulate the execution of the traffic control strategy on the corresponding traffic equipment in the physical system. According to the difference comparison result between the actual execution result and the simulated execution result fed back, optimize and adjust the traffic control strategy.

[0036] In this embodiment, the transmission of the traffic control strategy adopts a priority queue mechanism. Control strategies with high real-time requirements such as traffic signal timing plans are set as the highest priority, encrypted with quantum keys, and then sent to physical devices; non-real-time strategies such as route guidance instructions adopt an asynchronous transmission mode. A quantum key decryption module is built into the physical traffic equipment, and it supports regular key dynamic updates and device identity authentication.

[0037] The intelligent traffic collaborative control optimization method based on quantum key distribution and digital twin provided by the embodiment of the present invention deploys quantum communication devices at relevant nodes of the physical traffic system, uses quantum key distribution technology to establish a quantum secure communication network, provides a reliable security guarantee for the transmission of traffic data and spatial information, fundamentally solves the security risks of traditional encryption methods in the face of complex network attacks, and ensures the authenticity and reliability of the data of the digital twin mapping model used to construct the operation status of the mapped physical traffic system. Then, the digital twin mapping model is connected to the quantum computing environment, uses a preset quantum algorithm to generate traffic control strategies, and synchronously transmits the traffic control strategies to the traffic equipment of the physical traffic system and the digital twin mapping model through the quantum secure communication network. While the strategy is being executed on the physical device, it is also simulated in the digital twin mapping model. By comparing the differences between the actual execution result and the simulated execution result, problems that may exist in the actual traffic control application of the control strategy can be discovered in a timely manner, and the strategy can be optimized and adjusted, realizing the dynamic optimization of the traffic control strategy and enabling the control strategy to be adjusted in a timely manner according to the real-time changes of the traffic system, thereby significantly improving the operation management efficiency of the traffic system, improving the utilization rate of road resources to a certain extent, and alleviating traffic congestion.

[0038] In some embodiments, the deployment of quantum communication devices at relevant nodes of the physical traffic system and the use of quantum key distribution technology to establish a communication link to form a quantum secure communication network (step S1) include: S11. Select key nodes in the transportation system and install quantum communication devices with single-photon emission and reception functions at the key nodes. Among them, the key nodes at least include: intersection control centers, designated section monitoring points, and transportation hub stations, and the quantum communication devices support polarization state modulation and basis vector matching measurement functions. S12. Use a true random number generator to generate multiple random control parameters, and dynamically adjust the polarization state of the photons emitted by the single-photon source based on the random control parameters, so that the polarization state of the photons presents at least one of the phases of horizontal, vertical, +45°, and -45°. S13. According to the predetermined decoy state protocol, the transmitting end of the quantum communication device randomly sends signal state photons and decoy state photons according to a preset ratio. Among them, the signal state photons include the above four polarization state phases and are used to generate quantum keys, and the decoy state photons are used for data security monitoring. S14. Perform polarization state measurement on the received photons based on the polarization state phase synchronized with the transmitting end, and filter out valid data according to the basis vector matching rule. Among them, the basis vector matching rule is configured to filter and retain the photons with the polarization state phase of the transmitting end matching the polarization state phase of the receiving end. S15. Analyze the bit error rate and gain of the signal state and decoy state photons in the valid data. If the bit error rate of the decoy state photons increases compared with the bit error rate of the signal state photons, and the gain of the decoy state photons decreases, it is determined that there is a wiretapping risk in the channel, and the generation and distribution of the current quantum key are interrupted. S16. If it is determined that the channel is secure, a shared quantum key is generated based on the signal state photons in the valid data. S17. Establish an end-to-end quantum secure communication link according to the shared quantum key. In this embodiment, the quantum communication device can adopt products of the Huawei QKD-9000 series. The device has two independent quantum channels built-in and can realize automatic switching of the primary and backup links. Among them, the average photon number of the signal state photons is set to 0.6, and the average photon numbers of the decoy state photons are 0.2 and 0.01 respectively.

[0039] In the channel security determination link, statistical analysis is performed on the signal state and decoy state photons in the valid data. Considering the natural bit error rate fluctuation of the quantum communication link at different distances, a dynamic bit error rate threshold calculation formula is set: dynamic bit error rate threshold = signal state bit error rate × (1 + α × link distance / 10); where α is the bit error rate correction coefficient, and according to the actual environment test data, the value range is 0.1 - 0.3. When the bit error rate of the decoy state photons exceeds this threshold and the gain decreases by more than 10%, it is determined that there is a wiretapping risk in the channel, and the current key generation process is immediately interrupted, and the backup quantum channel switching mechanism is triggered to start; if the channel is secure, a 256-bit shared quantum key is generated from the signal state photons. After the generation of the shared quantum key, the shared quantum key is securely distributed to each node's physical device, and an operation mechanism for storage, update, and destruction is configured to establish an end-to-end quantum secure communication link, ensuring the authenticity and integrity of the link.

[0040] Specifically, the traffic data includes: vehicle position, speed, and traffic flow, and the spatial information includes: road geometric structure; Transmitting the traffic data and spatial information through the quantum secure communication network, and constructing a traffic digital twin mapping model based on the traffic data and spatial information (step S2), including: S21. Based on the quantum encryption hash algorithm, perform integrity verification on the received traffic data and spatial information to check whether the data has been tampered with during transmission; S22. Based on the verified traffic data and spatial information, perform data integration to construct a digital twin mapping model for real-time mapping of the operating state of the physical traffic system. The operating state of the traffic system includes: traffic flow distribution, signal light working state, and road traffic conditions. The collection and transmission of traffic data and spatial information adopt a distributed heterogeneous sensor network architecture. Millimeter-wave radars installed above the road continuously monitor the vehicle position and speed, and output data once every predetermined number of seconds, and video monitoring statistically outputs traffic flow data in real time.

[0041] After receiving the traffic data and the spatial information of the corresponding road section, the traffic control center performs integrity verification on the data based on the quantum encryption hash algorithm. For example, for each data packet, a 128-bit random seed is generated by a quantum random number generator and input into the hash function together with the data packet content to generate a 256-bit hash value. The generated hash value is compared bit by bit with the hash value attached to the data packet. If they are inconsistent, it is determined that the data has been tampered with during transmission, and the data retransmission mechanism is triggered.

[0042] Due to the possible differences in the data collection frequencies and time bases of different types of data, it is necessary to perform multi-source data calibration processing on traffic data and spatial information. In the data integration stage, the dynamic traffic data and static spatial information are aligned by timestamps, and a common coordinate system is used to unify the spatial benchmarks such as road section coordinates and intersection shapes to ensure that the two types of data are in the same spatio-temporal system. Specifically, in the time dimension, a linear interpolation algorithm is used to unify data with different frequencies to a time resolution of 1 second; in the spatial dimension, the vehicle position data is matched with the road geometric structure data to clarify the specific road section and lane where each vehicle is located. Then, through a spatial clustering analysis algorithm such as the DBSCAN algorithm, traffic flow distribution regions are divided based on the vehicle position data, and the congestion index of each region is calculated in combination with the traffic flow data; the real-time status data of the traffic lights is obtained from the traffic signal controller to update the working status of the traffic lights; the road traffic conditions are evaluated by integrating the road geometric structure and real-time traffic flow data, and finally a digital twin mapping model that can reflect the operating status of the physical traffic system of this expressway in real time is constructed. In some embodiments, data integration is performed based on the verified traffic data and spatial information to construct a digital twin mapping model for real-time mapping of the operating status of the physical traffic system, including: performing multi-source data calibration processing on the verified traffic data and spatial information based on timestamps so that the traffic data and spatial information are in the same spatio-temporal benchmark system; Among them, for dynamic traffic data and spatial information with different frequencies, relying on the high-precision time synchronization protocol based on quantum entanglement in the quantum secure communication network, a linear interpolation algorithm accelerated by quantum parallel computing is used for time alignment. In the process of unifying the spatial benchmark, a coordinate transformation algorithm based on quantum state superposition is used to convert the road section coordinates and intersection shape data in the road geometric structure into quantum states, and the road set structure parameters in quantum state are parallelly transformed to the Universal Transverse Mercator (UTM) projection coordinate system in the quantum computing environment through quantum gate operations, avoiding error accumulation in traditional GIS conversions, thereby improving the spatial coordinate calibration accuracy. The calibrated dynamic traffic data is statistically analyzed by a sliding window according to road sections to calculate the vehicle position, average vehicle speed, and traffic flow volatility characteristics within a predetermined time interval; and, the number of lanes, road curvature, and slope characteristics are extracted from the road geometric structure, and dimensionality reduction processing is performed on the obtained characteristics, and they are fused to generate a feature vector group containing each of the above characteristics; In this embodiment, the quantum principal component analysis (QPCA) algorithm is used for dimensionality reduction processing. By recombining and refining the eigenvectors of the original dimension, it is possible to improve the computational efficiency while retaining most of the information entropy. Specifically, the number of lanes, road curvature, and slope features are extracted from the road geometric structure, and dimensionality reduction processing is performed on the obtained features. A feature vector group containing each of the above features is generated through fusion, including: concatenating the dynamic features corresponding to each road segment and time window, including vehicle position, average vehicle speed, and traffic flow volatility, with the static features, namely the number of lanes, road curvature, and slope, column by column to form an original feature vector with a dimension of D = 6. All the original feature vectors are used to form a feature matrix, and zero-mean normalization processing is performed; the covariance matrix of the normalized feature matrix is calculated, eigenvalues and eigenvectors are obtained through eigenvalue decomposition, and the first k principal components are selected according to a preset dimensionality reduction target to form a transformation matrix; the normalized feature matrix is projected onto a k-dimensional subspace to achieve dimensionality reduction, and then the dimensionality-reduced feature vectors are bound to the original road segment identifier and time window information to generate a feature vector group containing spatio-temporal attributes and key features. Among them, the spatio-temporal attributes include timestamps and road segment identifier IDs, and the key features refer to the principal component features retained after dimensionality reduction, which are selected through principal component analysis from the original features such as vehicle position distribution, average vehicle speed, traffic flow volatility, number of lanes, road curvature, and slope, so as to retain to the greatest extent the traffic information in the original data that is of important value for traffic flow analysis, modeling, and prediction. When constructing a digital twin mapping model subsequently, a feature vector group obtained by fusing the original six features and the key features obtained after dimensionality reduction can be used as the basis for model construction and parameter adjustment. After dimensionality reduction, the method further includes: performing normalization processing on the dimensionality-reduced feature vector group, such as Z-score normalization, to eliminate the influence of dimensional differences on subsequent modeling.

[0043] Based on the feature vector group, a node-edge network model of the traffic system is constructed in the form of a graph structure of nodes and edges, where the nodes include intersections and ramps, and the edges include road segments and the number of lanes and speed limit attributes associated with the road segments; based on a preset three-dimensional modeling technology, spatial coordinate mapping is performed on the node-edge network to generate a three-dimensional road network framework containing geographic information; In this embodiment, the graph database Neo4j can be used as a storage and management platform. The intersection node is defined as an entity containing attributes such as signal phase parameters, turning traffic flow statistics, and queuing length thresholds; the ramp node is associated with information such as entrance and exit control strategies and merging zone lengths; the road segment edge stores attributes such as the number of lanes, speed limit, and lane width. Quick creation, update, and query operations of nodes and edges are realized through a preset query language. A three-dimensional road network framework is generated based on the Unity 3D engine, and a high-precision road geometric structure model is imported.

[0044] Based on the three-dimensional road network framework, a cellular automaton model is introduced to simulate vehicle car-following and lane-changing behaviors, a mapping relationship between signal phases and intersection nodes is established, and, combined with the influence information of road geometric structures on traffic flow, an initial signal control strategy is generated to preliminarily form a digital twin mapping model for real-time mapping of the operating state of the physical traffic system; wherein, the digital twin mapping model includes vehicle movement rules and traffic equipment control logics.

[0045] Specifically, based on the cellular automaton model, the lane is divided into cellular units of 0.5 m × 0.5 m, and various behavior rules such as vehicle acceleration, deceleration, car-following, and lane-changing are defined. Among them, the car-following rule and the lane-changing rule can be implemented using some existing models. For example, the car-following rule can adopt an optimized intelligent driving model (IDM), and the lane-changing rule is based on a safety gap model. The mapping between signal phases and intersection nodes is realized through an event-driven mechanism, and the generation of the initial signal control strategy is optimized using a genetic algorithm. Taking the average vehicle delay time, queue length, and traffic capacity as the objective functions, the optimal parameter combination is obtained through multiple rounds of iterative evolution to form a digital twin mapping model.

[0046] In some embodiments, the synchronization is transmitted to the digital twin mapping model to simulate the execution of the traffic control strategy on the corresponding traffic equipment in the physical system. According to the difference comparison result between the actual execution result and the simulated execution result fed back, the traffic control strategy is optimized and adjusted (step S4), including: S41. Load the traffic control strategy parameters, simulate the traffic flow distribution and the change process of signal states after the strategy execution, and generate simulated execution result data including virtual vehicle trajectories and intersection traffic efficiency; S42. Align the time stamps of the actual execution result data and the simulated execution result data, and match the virtual and real data sequences based on the dynamic time warping algorithm; S43. Calculate the difference comparison index between the actual execution result and the simulated execution result according to three dimensions: traffic flow distribution coincidence degree, signal control efficiency, and path planning effect; wherein, the traffic flow distribution coincidence degree is used to characterize the spatial overlap rate between the actual congestion area and the simulated congestion area, the signal control efficiency is used to characterize the difference in average vehicle delay time between the actual execution and the simulated execution, and the path planning effect is used to characterize the deviation distance between the actual navigation path and the simulated optimal path; S44. Optimize and adjust the traffic control strategy parameters according to the difference comparison index, and the traffic control strategy parameters include signal timing plans and path planning instructions.

[0047] When the traffic control center generates a new traffic control strategy based on quantum computing, including adjusting the signal timing at intersections and path planning instructions, the strategy will be synchronously transmitted to physical traffic devices and the digital twin mapping model. The control strategy parameters need to be consistent between the physical and virtual ends.

[0048] In the digital twin mapping model, the system first loads the traffic control strategy parameters. For example, if the new strategy adjusts the signal timing at a crossroads from "40 seconds of green light in the north-south direction and 30 seconds in the east-west direction" to "50 seconds in the north-south direction and 20 seconds in the east-west direction", the model will, based on this parameter, combine the following rules: the car-following and lane-changing rules of vehicles in the cellular automaton model, the lane attributes of the road section, and the real-time traffic flow distribution data, to simulate the traffic flow evolution process within the next 30 minutes after the strategy is executed. During the simulation, virtual vehicles will dynamically adjust their driving trajectories according to signal changes and road traffic conditions. The system will calculate in real time data such as the intersection traffic efficiency and traffic flow density distribution at each time step, and finally generate simulation execution result data including virtual vehicle trajectories and the traffic efficiency of each intersection.

[0049] At the same time, devices in the physical traffic system, such as intelligent signal lights and variable message signs, start to execute the new strategy. Sensors on the road, such as microwave radars and license plate recognition cameras, collect actual execution result data at a cycle of 30 seconds, including the actual time when vehicles pass through intersections, the real-time vehicle speeds on each road section, and the actual congestion areas formed.

[0050] To achieve an effective comparison with the data of the simulated execution results, the system synchronizes the two types of data based on the timestamp alignment mechanism. Suppose the time resolution of the simulated data is 100 milliseconds, while that of the actual data is 30 seconds. The system uses the Dynamic Time Warping (DTW) algorithm to match the feature points of the virtual and real data in the time series. For example, it precisely aligns the time when a vehicle passes through an intersection in the simulation with the time when the actual vehicle passes through the same location to ensure the consistency of the data dimensions. Then, the differences between the actual and simulated results are quantified from three dimensions. Taking the calculation of the traffic flow distribution matching degree as an example, the system conducts a spatial overlay analysis of the congested areas detected in reality and the congested areas generated by the simulation, and calculates the Jaccard coefficient between the two. Among them, the Jaccard coefficient is an index used to measure the similarity of two sets, which is equal to the number of elements in the intersection of the two sets divided by the number of elements in the union of the two sets. If the coefficient is 0.7, it means that the spatial overlap rate of the congested areas between the actual and simulated is 70%; in terms of signal control efficiency, the average delay time of the vehicles in the actual execution and the simulated execution is compared. If the actual average delay time is 2 minutes and the simulated value is 1.5 minutes, the difference in signal control efficiency is 0.5 minutes; for the path planning effect, the Hausdorff distance between the actual navigation path and the simulated optimal path is calculated to evaluate the degree to which the actual driving path of the vehicle deviates from the optimal path. Among them, the Hausdorff distance is an index used to measure the distance between two sets, and here it is used to represent the difference between the actual path and the simulated optimal path.

[0051] Based on the above difference comparison indicators, the quantum machine learning optimization mechanism is triggered. For example, if the traffic flow distribution matching degree is lower than 80% and the difference in signal control efficiency exceeds 1 minute, the system starts the quantum support vector machine algorithm. Using the difference indicators as the input, it iteratively optimizes the traffic control strategy parameters. Specifically, for the signal timing plan, the green light duration and phase switching sequence of each phase will be readjusted; for the path planning instructions, combined with the real-time road conditions and road capacity, a new vehicle guidance path will be generated. The optimized strategy is sent to the physical traffic devices for execution again through the quantum secure communication network and synchronized to the digital twin mapping model for a new round of simulation verification, forming a closed-loop control process of execution - simulation - comparison - optimization. Thus, the dynamic optimization of traffic control strategies is achieved.

[0052] Specifically, after calculating the differences in the traffic flow distribution matching degree, signal control efficiency, and path planning effect, in some embodiments, to adapt to different traffic scenarios, in this embodiment, instead of using fixed weights for policy optimization, the optimization priorities of each index are dynamically generated through the principle of quantum state superposition. For example, during the morning rush hour, the traffic flow distribution matching degree has a more significant impact on congestion alleviation, and the system automatically increases its weight to 0.6, the signal control efficiency to 0.3, and the path planning effect to 0.1; while in the case of an unexpected accident, the weight of the signal control efficiency can be temporarily increased to 0.7 to ensure that the vehicle delay problem is prioritized.

[0053] Therefore, in some embodiments, the optimizing and adjusting the traffic control strategy parameters according to the difference comparison index includes: dynamically allocating the optimization weights of each dimension index through quantum state superposition, and the weights are generated by a quantum classifier according to real-time traffic scenario labels to adapt to the differential optimization of policy parameters by different quantum algorithms, where the traffic scenario labels include: morning rush hour and evening rush hour; and calling the corresponding quantum algorithm for policy parameter adjustment according to the dynamic weights.

[0054] Specifically, the difference indicators of the three dimensions are encoded into a quantum superposition state. In this embodiment, the basic states respectively correspond to the traffic flow distribution matching degree of C, the signal control efficiency of E, and the path planning effect of P.

[0055] In quantum mechanics, a quantum bit can be in a superposition state, that is, representing multiple states simultaneously. In this embodiment, by encoding the three indicators into the form of a quantum superposition state, the weight coefficients corresponding to each basic state are a / b / c respectively, and the sum of their squares is equal to 1, which is used to characterize the proportional weight of each indicator in the superposition state, corresponding to the traffic flow distribution matching degree, signal control efficiency, and path planning effect respectively. In this example, by adopting quantum encoding, the characteristics of quantum computing, such as parallel computing ability, can be utilized to process and analyze the difference indicators more efficiently to optimize tasks related to traffic control strategies, so as to find the optimal traffic control solution faster.

[0056] The digital twin mapping model monitors real-time traffic scenario labels, such as "morning rush hour", "accident congestion", and "large event", etc., and inputs them into the quantum classifier. When the "accident congestion" label is detected, the quantum classifier outputs a specific rotation angle, increasing the weight b of the signal control efficiency to 0.7, decreasing the traffic flow distribution matching degree a to 0.2, and keeping the path planning effect c at 0.1. When the traffic flow distribution weight is high, that is, a 2> 0.5, the quantum local search algorithm is enabled to focus on optimizing the phase difference of signal light timing, shortening the green light start-up time difference between adjacent intersections by 5 seconds, and forming a regional "green wave band". Among them, the green wave band refers to within a specific regional scope, through scientific coordination settings of traffic signal lights, enabling vehicles to encounter green lights at a series of consecutive intersections when driving in this area; when the signal control weight is high, that is, b 2 > 0.6, the quantum genetic algorithm is triggered to preferentially optimize the average vehicle delay time. Multiple sets of timing schemes are generated through quantum crossover operations, and the optimal solution is selected after being simulated by the digital twin mapping model; when the path planning weight is high, c 2 > 0.4, the quantum ant colony algorithm is started, combined with the real-time road traffic capacity, to parallelly search for the optimal path at the quantum state level.

[0057] In a practical application test, the traffic collaborative control optimization method based on quantum key distribution and digital twin provided in this embodiment can reduce the average vehicle delay time by 25% - 35% and the congested area by 30% - 40%, significantly improving the operation efficiency of the traffic system. Since the digital twin and quantum computing technologies are organically integrated and applied to the intelligent traffic collaborative control scenario, to a certain extent, it can ensure the efficiency and accuracy of the strategy optimization process, effectively solving the problems of lagging strategy adjustment and poor optimization effect in traditional traffic control, thereby improving the operation management efficiency of the traffic system.

[0058] It should be noted that the intelligent traffic collaborative control optimization method based on quantum key distribution and digital twin provided in the above embodiments of this application can be solidified in a manufactured physical hardware device in the form of software or a program. When the software or program runs, the above method process can be reproduced.

[0059] Such as Figure 3 , this application also provides an intelligent traffic collaborative control optimization device based on quantum key distribution and digital twin, including: Quantum communication network construction module 21: used to deploy quantum communication devices at relevant nodes of the physical traffic system, and use quantum key distribution technology to establish a communication link to form a quantum secure communication network; Model construction module 22, which transmits traffic data and spatial information through the quantum secure communication network, and constructs a traffic digital twin mapping model based on the traffic data and spatial information; the traffic digital twin mapping model is configured to map and obtain the operating state of the physical traffic system corresponding to the traffic data and spatial information.

[0060] Strategy generation module 23, used to connect the digital twin mapping model to the quantum computing environment, and generate traffic control strategies based on the operating state of the physical traffic system using a preset quantum algorithm.

[0061] The policy execution optimization module 24 transmits the traffic control policy to the traffic devices in the physical traffic system through the quantum-secure communication network for execution, and simultaneously transmits it to the digital twin mapping model to simulate the execution of the traffic control policy on the corresponding traffic devices in the physical system. According to the comparison result of the difference between the actual execution result and the simulated execution result fed back, the traffic control policy is optimized and adjusted.

[0062] In some embodiments, the quantum communication network construction module 21 includes: The device deployment unit 211 selects key nodes in the traffic system and installs quantum communication devices with single-photon emission and reception functions at the key nodes; wherein, the key nodes at least include: intersection control centers, designated section monitoring points, and transportation hub stations, and the quantum communication devices support polarization state modulation and basis vector matching measurement functions; The polarization state regulation unit 212 is used to generate a plurality of random control parameters by using a true random number generator, and dynamically regulate the polarization state of the photons emitted by the single-photon source based on the random control parameters, so that the polarization state of the photons presents at least one of the phases of horizontal, vertical, +45°, and -45°; The photon sending unit 213 is used to randomly send signal-state photons and decoy-state photons according to a predetermined decoy-state protocol at a preset ratio by the transmitting end of the quantum communication device; wherein, the signal-state photons include the above four polarization state phases and are used to generate quantum keys, and the decoy-state photons are used for data security monitoring; The measurement and screening unit 214 is used to perform polarization state measurement on the received photons based on the polarization state phase synchronized with the transmitting end, and screen out valid data according to the basis vector matching rule; wherein, the basis vector matching rule is configured to screen and retain the photons with the polarization state phase of the transmitting end matching the polarization state phase of the receiving end; The security determination unit 215 is used to analyze the bit error rate and gain of the signal-state and decoy-state photons in the valid data. If the bit error rate of the decoy-state photons increases compared with the bit error rate of the signal-state photons, and the gain of the decoy-state photons decreases, it is determined that there is a risk of eavesdropping on the channel, and the generation and distribution of the current quantum key are interrupted; if the channel is determined to be secure, a shared quantum key is generated based on the signal-state photons in the valid data; The link establishment unit 216 is used to establish an end-to-end quantum-secure communication link according to the shared quantum key.

[0063] In some embodiments, the traffic data includes: vehicle position, speed, and traffic flow, and the spatial information includes: road geometric structure; the model construction module is specifically configured to: Based on the quantum encryption hash algorithm, perform integrity verification on the received traffic data and spatial information to check whether the data is tampered with during transmission; Integrate data based on the verified traffic data and spatial information, and construct a digital twin mapping model for real-time mapping of the operating state of the physical traffic system. The operating state of the traffic system includes: traffic flow distribution, signal light working state, and road traffic conditions.

[0064] The embodiment of the present application also provides an electronic device, such as Figure 4 As shown, the electronic device provided by the embodiment of the present invention may include: a housing 51, a processor 52, a memory 53, a circuit board 54, and a power supply circuit 55. Among them, the circuit board 54 is arranged inside the space surrounded by the housing 51, and the processor 52 and the memory 53 are arranged on the circuit board 54; the power supply circuit 55 is used to supply power to each circuit or device of the above-mentioned electronic device; the memory 53 is used to store executable program codes; the processor 52 runs the program corresponding to the executable program code by reading the executable program code stored in the memory 53, and is used to execute the traffic collaborative control optimization method based on quantum key distribution and digital twin provided by any of the foregoing embodiments.

[0065] For the specific execution process of the above steps by the processor 52 and the further steps executed by the processor 52 by running the executable program code, reference may be made to the description of the foregoing embodiments, which will not be repeated here.

[0066] In summary, the traffic collaborative control optimization method and device provided by the embodiment of the present application, by organically integrating quantum key distribution technology, digital twin technology, and quantum computing technology, form a complete intelligent traffic collaborative control system, which can solve the problems of insecure traffic data transmission and insufficient accuracy of control strategies in traditional intelligent traffic systems, thereby significantly improving the traffic operation management efficiency and safety. Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0067] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A traffic collaborative control optimization method based on quantum key distribution and digital twin, characterized in that, The method includes: Deploy quantum communication devices at relevant nodes of the physical transportation system, and use quantum key distribution technology to establish communication links to form a quantum secure communication network; Transmit traffic data and spatial information through the quantum secure communication network, and construct a traffic digital twin mapping model based on the traffic data and spatial information; the traffic digital twin mapping model is configured to map and obtain the operating state of the physical transportation system corresponding to the traffic data and spatial information; Connect the digital twin mapping model to a quantum computing environment, and use a preset quantum algorithm to generate traffic control strategies based on the operating state of the physical transportation system; Transmit the traffic control strategies through the quantum secure communication network to the traffic devices in the physical transportation system for execution, and synchronously transmit them to the digital twin mapping model to simulate the execution of the traffic control strategies on the corresponding traffic devices in the physical system. According to the difference comparison result of the actual execution result and the simulated execution result fed back, optimize and adjust the traffic control strategies.

2. The method according to claim 1, wherein The deploying quantum communication devices at relevant nodes of the physical transportation system, using quantum key distribution technology to establish communication links, and forming a quantum secure communication network includes: Select key nodes in the transportation system and install quantum communication devices with single-photon emission and reception functions at the key nodes; wherein, the key nodes at least include: intersection control centers, designated section monitoring points, and transportation hub stations, and the quantum communication devices support polarization state modulation and basis vector matching measurement functions; Use a true random number generator to generate multiple random control parameters, and dynamically adjust the polarization state of the photons emitted by the single-photon source based on the random control parameters, so that the polarization state of the photons presents at least one of the phases of horizontal, vertical, +45°, and -45°; According to the predetermined decoy state protocol, the transmitting end of the quantum communication device randomly sends signal state photons and decoy state photons at a preset ratio; wherein, the signal state photons include four polarization state phases and are used to generate quantum keys, and the decoy state photons are used for data security monitoring; Perform polarization state measurement on the received photons based on the polarization state phase synchronized with the transmitting end, and filter out valid data according to the basis vector matching rule; wherein, the basis vector matching rule is configured to filter and retain the photons with the polarization state phase of the transmitting end matching the polarization state phase of the receiving end; Analyze the bit error rate and gain of the signal state and decoy state photons in the valid data. If the bit error rate of the decoy state photons increases compared with the bit error rate of the signal state photons, and the gain of the decoy state photons decreases, it is determined that there is a wiretapping risk in the channel, and the generation and distribution of the current quantum key are interrupted; If it is determined that the channel is secure, generate a shared quantum key based on the signal state photons in the valid data; Establish an end-to-end quantum secure communication link according to the shared quantum key.

3. The method according to claim 1, wherein The traffic data includes: vehicle position, speed, and traffic flow, and the spatial information includes: road geometric structure; The transmitting traffic data and spatial information through the quantum secure communication network, and constructing a traffic digital twin mapping model based on the traffic data and spatial information includes: Based on a quantum encryption hash algorithm, perform integrity verification on the received traffic data and spatial information to check whether the data has been tampered with during transmission; Based on the verified traffic data and spatial information, perform data integration to construct a digital twin mapping model for real-time mapping of the operating state of the physical traffic system. The operating state of the traffic system includes: traffic flow distribution, signal light working state, and road traffic conditions.

4. The method according to claim 3, wherein Based on the verified traffic data and spatial information, perform data integration to construct a digital twin mapping model for real-time mapping of the operating state of the physical traffic system, including: performing multi-source data calibration processing on the verified traffic data and spatial information based on timestamps to enable the traffic data and spatial information to be in the same spatio-temporal reference system; Perform sliding window statistics on the calibrated dynamic traffic data by section, calculate the vehicle position, average vehicle speed, and traffic flow volatility characteristics within a predetermined time interval; and extract the number of lanes, road curvature, and slope characteristics from the road geometric structure, perform dimensionality reduction processing on the obtained characteristics, and fuse them to generate a feature vector group containing each of the above characteristics; Based on the feature vector group, construct a node-edge network model of the traffic system in the form of a graph structure of nodes and edges, where the nodes include intersections and ramps, and the edges include sections and the number of lanes and speed limit attributes associated with the sections; Based on the preset 3D modeling technology, perform spatial coordinate mapping on the node-edge network to generate a 3D road network framework containing geographic information; On the basis of the 3D road network framework, introduce a cellular automaton model to simulate vehicle following and lane-changing behaviors, establish a mapping relationship between signal phases and intersection nodes, and combine the influence information of the road geometric structure on traffic flow to generate an initial signal control strategy, initially forming a digital twin mapping model for real-time mapping of the operating state of the physical traffic system; where the digital twin mapping model contains vehicle movement rules and traffic equipment control logic.

5. The method according to claim 4, characterized in that, Synchronously transmit to the digital twin mapping model to simulate the execution of the traffic control strategy on the corresponding traffic equipment in the physical system, and optimize and adjust the traffic control strategy according to the difference comparison result between the actual execution result and the simulated execution result, including: Load traffic control strategy parameters, simulate the process of traffic flow distribution and signal light state change after the strategy execution, and generate simulated execution result data including virtual vehicle trajectories and intersection passing efficiency; Align the timestamps of the actual execution result data and the simulated execution result data, and match the virtual and real data sequences based on the dynamic time warping algorithm; According to three dimensions of traffic flow distribution coincidence degree, signal control efficiency, and path planning effect, calculate the difference comparison index between the actual execution result and the simulated execution result; where the traffic flow distribution coincidence degree is used to characterize the spatial overlap rate between the actual congestion area and the simulated congestion area, the signal control efficiency is used to characterize the difference in average vehicle delay time between the actual execution and the simulated execution, and the path planning effect is used to characterize the deviation distance between the actual navigation path and the simulated optimal path; Optimize and adjust the traffic control strategy parameters according to the difference comparison index, where the traffic control strategy parameters include signal timing plans and route planning instructions.

6. The method according to claim 5, characterized in that, The control strategy parameters need to be consistent between the physical end and the virtual end.

7. An intelligent transportation collaborative control device based on quantum key distribution and digital twin, characterized in that, It includes: A quantum communication network construction module, which is used to deploy quantum communication devices at relevant nodes of the physical traffic system, and use quantum key distribution technology to establish communication links to form a quantum secure communication network; A model construction module, which transmits traffic data and spatial information through the quantum secure communication network, and constructs a traffic digital twin mapping model based on the traffic data and spatial information; the traffic digital twin mapping model is configured to map and obtain the operating state of the physical traffic system corresponding to the traffic data and spatial information; A strategy generation module, which is used to connect the digital twin mapping model to a quantum computing environment, and generate a traffic control strategy based on the operating state of the physical traffic system using a preset quantum algorithm; A strategy execution optimization module, which transmits the traffic control strategy to the traffic devices in the physical traffic system through the quantum secure communication network for execution, and synchronously transmits it to the digital twin mapping model to simulate the execution of the traffic control strategy on the corresponding traffic devices in the physical system. According to the difference comparison result of the actual execution result and the simulated execution result fed back, optimize and adjust the traffic control strategy.

8. The device according to claim 1 of the device claim, characterized in that, The quantum communication network construction module includes: A device deployment unit, which is used to select key nodes in the traffic system and install quantum communication devices with single-photon emission and reception functions at the key nodes; among them, the key nodes at least include: intersection control centers, designated section monitoring points, and transportation hub stations, and the quantum communication devices support polarization state modulation and basis vector matching measurement functions; A polarization state regulation unit, which is used to generate multiple random control parameters using a true random number generator, and dynamically regulate the polarization state of photons emitted by a single-photon source based on the random control parameters, so that the polarization state of photons presents at least one of the phases of horizontal, vertical, +45°, and -45°; A photon sending unit, which is used to randomly send signal-state photons and decoy-state photons according to a predetermined decoy state protocol by the transmitting end of the quantum communication device at a preset ratio; among them, the signal-state photons include four polarization state phases and are used to generate quantum keys, and the decoy-state photons are used for data security monitoring; A measurement and screening unit, which is used to perform polarization state measurement on received photons based on the polarization state phase synchronized with the sending end, and screen out valid data according to the basis vector matching rule; among them, the basis vector matching rule is configured to screen and retain photons with matching polarization state phases between the sending end and the receiving end; A security determination unit, which is used to analyze the bit error rate and gain of the signal-state and decoy-state photons in the valid data. If the bit error rate of the decoy-state photons increases compared with the bit error rate of the signal-state photons, and the gain of the decoy-state photons decreases, it is determined that there is a wiretapping risk in the channel, and the generation and distribution of the current quantum key are interrupted; if the channel is determined to be secure, a shared quantum key is generated based on the signal-state photons in the valid data; A link establishment unit, which is used to establish an end-to-end quantum secure communication link according to the shared quantum key.

9. The method according to claim 1, characterized in that, The traffic data includes: vehicle position, speed, and traffic flow, and the spatial information includes: road geometric structure; the model construction module is specifically configured to: Based on the quantum encryption hash algorithm, perform integrity verification on the received traffic data and spatial information to check whether the data has been tampered with during transmission; Based on the verified traffic data and spatial information, perform data integration to construct a digital twin mapping model for real-time mapping of the operating state of the physical traffic system, where the operating state of the traffic system includes: traffic flow distribution, signal light working state, and road traffic conditions.

10. An electronic device, characterized in that, The electronic device includes: a housing, a processor, a memory, a circuit board, and a power supply circuit. Among them, the circuit board is arranged inside the space enclosed by the housing, and the processor and the memory are arranged on the circuit board; the power supply circuit is used to supply power to each circuit or device of the above-mentioned electronic device; the memory is used to store executable program codes; the processor runs the program corresponding to the executable program codes by reading the executable program codes stored in the memory, and is used to execute the method described in any one of the preceding claims 1 to 6.

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